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Are Amazon Ads eating away our Organic Sales?

Title: Correlation Analysis of Ad and Organic Sales to Identify Potential Cannibalization

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1. Introduction

This report outlines the methodology and findings of a correlation analysis conducted to investigate whether advertising (ad) sales are cannibalizing organic sales at the ASIN level. By examining daily sales data over 117 days (Nov 1,2024 to Feb 25, 2025) , we aimed to identify where strong negative correlations exist between ad and organic sales and how that insight can guide campaign-level optimizations.

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2. Objectives

Assess Correlation at ASIN Level: Determine if a relationship (positive or negative) exists between ad sales and organic sales for each ASIN.

Identify Potential Cannibalization: Pinpoint ASINs with a strong negative correlation, suggesting that increased ad sales may be reducing organic sales.

Translate Findings to Campaign Level: Aggregate ASIN-level correlations to identify campaigns that potentially require optimization (“cleanup”).

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3. Methodology

Data Collection & Preparation

Timeframe: 117 days of daily ad sales and organic sales data were collected for each ASIN.

Data Points:

Ad Sales per ASIN: The total daily ad-attributed sales.

Organic Sales per ASIN: The total daily non-ad sales.

Spend per ASIN: The total daily ad spend for each ASIN.

Correlation Computation

Correlation Factor: For each ASIN, the correlation between daily ad sales and daily organic sales was calculated. A negative correlation suggests that as one metric rises, the other tends to fall.

Threshold for Cannibalization: ASINs with a “strong negative correlation” (often indicated by correlation coefficients well below zero, e.g., -0.5 or lower, depending on the business context) were flagged as potential cannibalization cases.

Campaign-Level Aggregation

Tagging ASIN Correlation to Campaigns: Since each ASIN can appear in multiple campaigns, the ASIN-level correlation was “tagged” or associated with each campaign that drove its ad sales.

Pivot at Campaign Level: Data was pivoted by campaign to show:

Number of ASINs in each campaign

Cumulative ad spend for those ASINs within the campaign

Total ad sales driven by that campaign

Average correlation for the ASINs in that campaign

Proxy for Campaign Correlation: While correlation was computed only at the ASIN level, the average correlation score per campaign serves as a proxy for the overall impact of that campaign on organic sales.

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4. Key Findings

Negative Correlation Identified

86 ASINs with sufficient ad spend demonstrated a strong negative correlation between ad and organic sales. These ASINs are the primary candidates for potential cannibalization.

Campaigns Requiring Cleanup

By aggregating ASIN-level data to campaigns, we identified specific campaigns whose associated ASINs exhibited notably negative average correlations. These are prime targets for optimization or “cleanup,” such as pausing certain keywords, adjusting budgets, or modifying bids.

Insight into Resource Allocation

High ad spend on ASINs showing a strong negative correlation may not be driving incremental sales. Instead, it may be shifting existing demand from organic to paid channels.

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5. Benefits & Applications

Cost Optimization:

By identifying cannibalizing campaigns and ASINs, the Advertising team can reallocate or reduce ad spend where it may not be generating incremental sales.

Incremental Sales Growth:

Adjusting or turning off ads for ASINs with strong negative correlation can potentially boost organic sales, as users who would have purchased organically are no longer directed through a paid listing.

More Informed Budget Decisions:

This analysis helps decide which campaigns should be optimized further and which can be reduced or eliminated to maximize ROI.

Future Analytics Enhancements:

The approach of aggregating ASIN-level correlations to campaign-level is a practical workaround. Over time, more sophisticated models (e.g., multi-variate analysis or econometric models) can refine these insights.

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6. Next Steps

Campaign Cleanup

Action by Ads Team: Identify and pause or reduce bids on campaigns (and specific keywords) that target ASINs with strongly negative correlations.

Monitor Performance Post-Adjustment

Evaluate subsequent changes in both organic and ad sales to validate the impact on overall profitability and volume.

Refine Thresholds

Continue to test and refine what qualifies as “strong negative correlation” and adapt the approach based on observed outcomes (conversion rates, overall profitability, etc.).

Expand Analysis

Investigate additional factors (e.g., pricing changes, seasonality, competitor behavior) that might influence the correlation between ad and organic sales.

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7. Conclusion

This correlation analysis provides actionable insights into how ad spend can sometimes cannibalize organic sales for certain ASINs. By aggregating ASIN-level correlations to campaigns, we can identify where resource reallocation or reduction could yield a net positive return. Implementing these findings—via focused campaign “cleanup”—should lead to better optimization of ad budgets, improved organic visibility, and ultimately a more profitable overall strategy.

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